Automatic Configuration of Deep Neural Networks with EGO Machine Learning

Designing the architecture for an artificial neural network is a cumbersome task because of the numerous parameters to configure, including activation functions, layer types, and hyper-parameters. With the large number of parameters for most networks nowadays, it is intractable to find a good configuration for a given task by hand. In this paper an Efficient Global Optimization (EGO) algorithm is adapted to automatically optimize and configure convolutional neural network architectures. A configurable neural network architecture based solely on convolutional layers is proposed for the optimization. Without using any knowledge on the target problem and not using any data augmentation techniques, it is shown that on several image classification tasks this approach is able to find competitive network architectures in terms of prediction accuracy, compared to the best hand-crafted ones in literature. In addition, a very small training budget (200 evaluations and 10 epochs in training) is spent on each optimized architectures in contrast to the usual long training time of hand-crafted networks. Moreover, instead of the standard sequential evaluation in EGO, several candidate architectures are proposed and evaluated in parallel, which saves the execution overheads significantly and leads to an efficient automation for deep neural network design.

Intelligent Path Prediction for Vehicular Travel

AI Magazine

The problem of predicting the motion of a vehicle has been investigated by several researchers. Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. In contrast, my dissertation (Krozel 1992)1 presents a methodology for intelligent path prediction, where predicting the motion of an observed vehicle is performed by reasoning about the decision-making strategy of the vehicle's operator.

Distributed optimization of deeply nested systems Machine Learning

In science and engineering, intelligent processing of complex signals such as images, sound or language is often performed by a parameterized hierarchy of nonlinear processing layers, sometimes biologically inspired. Hierarchical systems (or, more generally, nested systems) offer a way to generate complex mappings using simple stages. Each layer performs a different operation and achieves an ever more sophisticated representation of the input, as, for example, in an deep artificial neural network, an object recognition cascade in computer vision or a speech front-end processing. Joint estimation of the parameters of all the layers and selection of an optimal architecture is widely considered to be a difficult numerical nonconvex optimization problem, difficult to parallelize for execution in a distributed computation environment, and requiring significant human expert effort, which leads to suboptimal systems in practice. We describe a general mathematical strategy to learn the parameters and, to some extent, the architecture of nested systems, called the method of auxiliary coordinates (MAC). This replaces the original problem involving a deeply nested function with a constrained problem involving a different function in an augmented space without nesting. The constrained problem may be solved with penalty-based methods using alternating optimization over the parameters and the auxiliary coordinates. MAC has provable convergence, is easy to implement reusing existing algorithms for single layers, can be parallelized trivially and massively, applies even when parameter derivatives are not available or not desirable, and is competitive with state-of-the-art nonlinear optimizers even in the serial computation setting, often providing reasonable models within a few iterations.

Predicting a Mission Objective

AI Magazine

Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. With intelligent path prediction, the long-term mission objective of the vehicle is being predicted in addition to the short-term motion. Thus, when applied to predicting the motion of a car, an intelligent predictor will attempt to predict the final destination--say, for example, the vehicle appears to be going to the post office or the art museum--in addition to predicting which streets will be used. The theory is also applicable to predicting air vehicle travel, so that for a military application, the target (from a set of plausible targets) and the threat-avoidance policy (from a set of plausible policies), in addition to the route, can be predicted. The first investigation is to develop a method for identifying a decisionmaking strategy that seemingly explains the vehicle's motion.

Understanding and Robustifying Differentiable Architecture Search Artificial Intelligence

Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem. However, DARTS does not work robustly for new problems: we identify a wide range of search spaces for which DARTS yields degenerate architectures with very poor test performance. We study this failure mode and show that, while DARTS successfully minimizes validation loss, the found solutions generalize poorly when they coincide with high validation loss curvature in the space of architectures. We show that by adding one of various types of regularization we can robustify DARTS to find solutions with smaller Hessian spectrum and with better generalization properties. Based on these observations we propose several simple variations of DARTS that perform substantially more robustly in practice. Our observations are robust across five search spaces on three image classification tasks and also hold for the very different domains of disparity estimation (a dense regression task) and language modelling. We provide our implementation and scripts to facilitate reproducibility.